Causing An Effect: Activists, Uncertainty & Images of the Future
Bibliographic record
Abstract
Causing an Effect is a futures exhibition and research project that draws from individual foresight, design research, and design iction to build understanding for activists working in future-minded ways. Seeking to emphasize the work of Canadian and American activists, this project highlights and celebrates these bold citizens in their ability to unearth complex environmental problems that threaten the health and wellbeing of their community. The research aims to generate images of the future, give voice and build empathy for activists, and create a space for strategic conversation around the future of North American industrial communities. \n \nThree case studies were developed to understand activists working in Sarnia, Ontario, Canada; Aamjiwnaang First Nation, Ontario, Canada; and Love Canal (Niagara Falls), New York. The project explores how three ordinary people living in industrial communities transform into changemakers, overcoming uncertainty to make positive social and environmental change. The research methodology began with a formal literature review followed by primary research based on the Ethnographic Futures Research (EFR) method. The insights and output were then processed and illustrated with the experiential futures method Reverse Archaeology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.018 | 0.016 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".